---
title: "COVID-19 | Tennessee"
output:
    flexdashboard::flex_dashboard:
      orientation: rows
      vertical_layout: scroll
      social: menu
      source_code: embed
knit: (function(input_file, encoding) {
  out_dir <- 'docs';
  rmarkdown::render(input_file,
 encoding=encoding,
 output_file=file.path(dirname(input_file), out_dir, 'index.html'))})
---
  

```{r setup, include=FALSE}
library(flexdashboard)
library(readr)
library(ggplot2)
library(tidyverse)
library(dplyr)
#Acquire Data####
#Load NY Times Data###
nyt_path = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv'

counties = read_csv(url(nyt_path)) #Originally contains all counties in US.

#Separate State
tn = counties[ which(counties$state =='Tennessee'),]
tn = tn[which(tn$date < '2020-03-31'),] #The Tennessee data from the new source has data starting March 31

#Tennessee data from online
tn_state = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/32b104abc5d841ca895de7f7c17fc4dc/data'

download.file(tn_state,'TN_COVID19_CountyDaily.xlsx') 

tn_daily =  readxl::read_excel('TN_COVID19_CountyDaily.xlsx',sheet=1) %>%
  filter(DATE > '2020-03-30') %>%
  select(DATE, COUNTY, TEST_POS, TEST_NEG, DEATHS_TOT) %>%
  filter(COUNTY != 'Balance') 

names(tn_daily) = c('Date', 'County', 'Positive', 'Negative', 'Death')

tn_daily$County = ifelse(tn_daily$County == 'Non-Tennessee Resident',
                         "Out of TN",
                         tn_daily$County)

tn_daily$County =ifelse(tn_daily$County == 'Dekalb', 
                        'DeKalb', 
                        tn_daily$County)

tn_daily$County =ifelse(tn_daily$County == 'VanBuren', 
                        'Van Buren', 
                        tn_daily$County)

tn_daily$County = sub('ff', "ff", tn_daily$County) #Coffee and Jefferson have some weird issues.

#Check counties
#levels(as.factor(tn_daily$County))

tn_daily$County = as.factor(tn_daily$County)
#tn_daily$Date = as.Date(tn_daily$Date, format = '%m/%d/%y')


#Merge NYT and Tn Daily dataframes####
tn_daily2 = tn_daily[,c('Date','County', 'Positive', 'Death')]
names(tn_daily2) = c('date','county', 'cases', 'deaths')
tn_daily2 = tn_daily2[!(tn_daily2$county =='Out of TN' | tn_daily2$county =='Pending'),]
tn_daily2 = tibble::add_column(tn_daily2, state = 'Tennessee', .after='county')

fips_daily =tn %>% group_by(county, fips) %>% tally()

tn_daily2 = left_join(tn_daily2, fips_daily[,1:2], by ='county')
##Row bind tn_daily (TN Health Dept) with tn
tn = rbind(tn, tn_daily2) #Rbind will automatically put the correct columns together. 


#Add population ####
#Get Population for counties in Tennessee
uscensus = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/county_pop_2019.csv'
tn_pop = read_csv(url(uscensus))
tn_pop = tn_pop[ which(tn_pop$State =='Tennessee'),]
tn_pop = tn_pop[-1,c(2:3)]
tn_pop$County = gsub(' County', '', tn_pop$County)
tn_pop$Population = as.numeric(tn_pop$Population)
tn_pop = tn_pop[, c('County', 'Population')]
names(tn_pop) = c('county', 'population')
#tn_pop[order(-tn_pop$population),]

##Combine tn (NYT) dataframe with Population
tn = left_join(tn, tn_pop, by='county')
tn$county = as.factor(tn$county)

#Calculate per million
tn['cases_per_million'] = (tn$cases/tn$population)*10^6

#Keep most recent for tn_daily
tn_daily = tn_daily %>% group_by(County) %>% top_n(1, Date)

#Clean the global environment###
rm(list=ls()[!ls() %in% c('tn', 'tn_daily')])



#Value Box Calculations
tn_ext =  readxl::read_excel('TN_COVID19_COUNTYDaily.xlsx',sheet=1) %>%
  top_n(1,DATE) %>%
  select(DATE:RATE_CHG_1DAY,RECOV_TOT:ACTIVE_NEW) %>%
  filter(COUNTY != 'Balance') 

tn_ext$COUNTY = ifelse(tn_ext$COUNTY == 'Non-Tennessee Resident',
                         "Out of TN",
                         tn_ext$COUNTY)

tn_ext$COUNTY =ifelse(tn_ext$COUNTY == 'Dekalb', 
                        'DeKalb', 
                        tn_ext$COUNTY)

tn_ext$COUNTY =ifelse(tn_ext$COUNTY == 'VanBuren', 
                        'Van Buren', 
                        tn_ext$COUNTY)

tn_ext$COUNTY = sub('ff', "ff", tn_ext$COUNTY) #Coffee and Jefferson have some weird issues.


tn_ext$COUNTY = as.factor(tn_ext$COUNTY)

#Total Cases

total_cases = sum(tn_ext$TEST_POS)
total_negative = sum(tn_ext$TEST_NEG)
total_death = sum(tn_ext$DEATHS_TOT)

total_recov = sum(tn_ext$RECOV_TOT)
active_cases = total_cases - total_death - total_recov #sum(tn_ext$ACTIVE_TOT)

total_tests = total_cases + total_negative



```

Data Visualizations by County
=======================================

Rows {data-width = 150}
-----------
### Confirmed Cases to Date

```{r}
#Total Positive Cases
cases_per = ((total_cases/total_tests)*100) %>% round(1) %>% paste0('%')
total_cases_vb = total_cases %>% formattable::comma(digits=0) %>% paste0(' (',cases_per,')') 
valueBox(value = total_cases_vb, icon='fa-user-plus', color='#002D65')
```

### Negative Tests 

```{r} 
#Total Negative Cases
negative_per = ((total_negative/total_tests)*100) %>% round(1) %>% paste0('%')
total_negative_vb = total_negative %>% formattable::comma(digits=0) %>% paste0(' (', negative_per,')') 
valueBox(value = total_negative_vb, icon='fa-user-minus', color='#CC0000')
```


Rows {data-width = 150}
-----------

### Recovered Cases: `r total_recov %>% formattable::comma(digits=0)`
```{r}
recov_label = (total_recov %>% formattable::comma(digits=0))
recov_per = ((total_recov/(total_cases))*100) %>% round(1)
gauge(recov_per, min=0, max = 100, symbol = '%')
```

### Active Cases: `r active_cases %>% formattable::comma(digits=0)`
```{r}
active_per = ((active_cases/(total_cases))*100) %>% round(1) 
gauge(active_per, min=0, max = 100, symbol = '%', 
      gaugeSectors(
        success = c(0,25), warning = c(26,100)))
```

### Total Deaths: `r total_death %>% formattable::comma(digits=0)` 

```{r} 
 #Total Deaths Cases
death_per = ((total_death/total_cases)*100) %>% round(1) %>% paste0('%')
gauge(death_per, min=0, max = 100, symbol = '%', 
      gaugeSectors(
        success = c(0,5), warning = c(6,100)))
```


Column {data-width=650}
-----------------------------------------------------------------------

### Cases across time in most populous counties

```{r}
library(plotly)
tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]


t_line = tn_pop_line =ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '2 day', date_labels = '%b %d')+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), panel.background = element_blank(), axis.line.x=element_line(), axis.line.y.left = element_line(), axis.text=element_text(face='bold'),axis.text.x = element_text(angle=45, hjust=1))+
  scale_color_brewer(palette = 'Spectral',direction=-1)
ggplotly(t_line)
```

### Cases across time in most populous counties {.mobile}

```{r}
tn_top =c('Shelby', 'Davidson', 'Knox', 'Hamilton', 'Rutherford', 'Williamson')
tn_top = tn[ tn$county %in% tn_top,]

ggplot(data=tn_top, aes(x=date, y=cases, color=county))+
  geom_line(size=1)+
  scale_x_date(expand = c(0,0), date_breaks = '1 week', date_labels = '%m-%d')+
  labs(x='', y='Cases')+
  theme(legend.title = element_blank(), 
        panel.background = element_blank(),
        axis.line.x=element_line(), 
        axis.line.y.left = element_line(), 
        axis.text=element_text(face='bold'),
        axis.text.x = element_text(angle=45, hjust=1),
        legend.position = c(0,.9),
        legend.justification = c(-0.1,.8))+
  scale_color_brewer(palette = 'Spectral',direction=-1)
```

Row {data-width=400}
-------------------------
### Cases rate 
```{r}
library(usmap)
library(viridis)
tn_geo =tn %>% group_by(county) %>% top_n(1,date)
tn_geo = tn_geo[!(tn_geo$county =='Unknown'),]
tn_geo$fips =fips(state = 'TN', county=tn_geo$county)

library(rjson)
url = 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
json_file <- rjson::fromJSON(file=url)

#Create map

fig <- plot_ly() %>% 
  add_trace(
    text = paste(tn_geo$county,' County'),
    hoverinfo = 'text',
    type='choroplethmapbox',
    geojson= json_file,
    locations=tn_geo$fips,
    z = tn_geo$cases_per_million,
    zmin=0,
    zmax = round(max(tn_geo$cases_per_million),-3),
    colorscale='Viridis',
    marker=list(line=list(
      width=0),
      opacity=0.9)) %>% 
  layout(mapbox=list(
    style="carto-positron",
    zoom =5.1,
    center=list(lon= -86.7816, lat=36.1627))) %>%
  colorbar(title = "Cases per million") 
fig

```


















### Case numbers by county
```{r}
#Trying Data table
tn_daily[,2:5] %>%
  DT::datatable(rownames = FALSE,
                colnames = c('County', 'Confirmed', 'Negative', 'Death'),
                options = list(pageLength = 10))
```

Column {data-width=350, data-height=400}
-----------------------------------------------------------------------

### Positive cases by counties with more than 20 cases

```{r}
tn_cases = tn_daily[which(tn_daily$Positive >20 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), c('County', 'Positive','Negative','Death')] #Remove where there are no cases

plot_ly(data=tn_cases,
        x=tn_cases$Positive,
        y=reorder(tn_cases$County, tn_cases$Positive),
        type='bar',
        orientation='h', 
        marker= list(color='#002D65')) %>%
  layout(xaxis = list(title= 'Count', 
                      zeroline = FALSE, 
                      showline = F, 
                      showticklabels = T, 
                      showgrid = T),
         yaxis = list(showgrid = FALSE, 
                      showline = FALSE, 
                      showticklabels = TRUE,
                      dtick=1,
                      tickfont = list(size=10)))
```

### Positive cases by county {.mobile}

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 20 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), c('County', 'Positive','Negative','Death')] #Remove where there are no cases

ggplot(data=tn_cases,aes(x=Positive, y=reorder(County,Positive)))+
  geom_col(fill='#002D65')+
  ylab('')+
  xlab('')+
  theme(panel.background = element_blank(),
        axis.line.x=element_line(), 
        axis.line.y.left = element_line(), 
        axis.text=element_text(face='bold'), 
        axis.ticks = element_blank())+
  scale_x_continuous(expand= c(0,0))+
  ggtitle("Counties with more than 20 cases")
```


### All outcomes by counties with more than 50 cases

```{r}
tn_cases = tn_daily[which(tn_daily$Positive > 50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), c('County', 'Positive','Negative','Death')] #Remove where there are no cases

plot_ly(data=tn_cases,
        x= reorder(tn_cases$County, tn_cases$Negative),
        y=tn_cases$Negative,
        type='bar',
        name='Negative Cases',
        marker= list(color='#002D65')) %>%
          add_trace(y = tn_cases$Positive,
                    name='Positive Cases',
                    marker = list(color='grey')) %>%
          add_trace(y = tn_cases$Death,
                    name='Deaths',
                    marker = list(color='#CC0000')) %>%
          layout(barmode = 'stack',
                 xaxis = list(showgrid = FALSE, 
                              showlilnee = FALSE, 
                              showticklabels = TRUE,
                              dtick=1,
                              tickfont =list(size=10)),
                 yaxis = list(title= 'Count', 
                              zeroline = FALSE, 
                              showline = F, 
                              showticklabels = T, 
                              showgrid = T),
                 hovermode = 'compare')
```

### All Cases by County {.mobile}

```{r}
library(plotly)
tn_cases = tn_daily[which(tn_daily$Positive > 50 & 
                            tn_daily$County != 'Pending'  &
                            tn_daily$County != 'Out of TN'), c('County', 'Positive','Negative','Death')] 

plot_ly(data=tn_cases,
        x= reorder(tn_cases$County, tn_cases$Negative),
        y=tn_cases$Negative,
        type='bar',
        name='Negative Cases',
        marker= list(color='darkblue')) %>%
  add_trace(y = tn_cases$Positive,
            name='Positive Cases',
            marker = list(color='grey')) %>%
  add_trace(y = tn_cases$Death,
            name='Deaths',
            marker = list(color='#CC0000')) %>%
  layout(barmode = 'stack',
         title='Counties with more than 50 cases',
         xaxis = list(showgrid = FALSE, 
                      showlilnee = FALSE, 
                      showticklabels = TRUE,
                      dtick=1,
                      tickfont =list(size=10)),
         yaxis = list(title= 'Count', 
                      zeroline = FALSE, 
                      showline = F, 
                      showticklabels = T, 
                      showgrid = T),
         hovermode = 'compare')
```

Data Visualizatons by Demographics
==================================

Column {data-width=350, data-height=450}
---------------------------
### Confirmed Cases by Age
```{r}
#Get US Census Data
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

age_census = readxl::read_excel('census_demographics.xlsx',sheet='Age')

#Get TN Data
tn_age = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/1bdfe86c38514c9c878241d5230d9a85/data'

download.file(tn_age,'TN_Age.xlsx') 

tn_age =  readxl::read_excel('TN_Age.xlsx',sheet=1) %>% 
  top_n(1,DATE) %>%
  select(DATE, AGE, TOT_CASE_COUNT, DEATHS_TOT)


names(tn_age) = c('Date', 'Age_Ranges', 'Count',  'Deaths')

tn_age$Age_Ranges = as.factor(tn_age$Age_Ranges)
tn_age$Case_Percent = round((tn_age$Count/sum(tn_age$Count))*100,1)
tn_age$Death_Percent =round((tn_age$Deaths/sum(tn_age$Deaths))*100,1)
tn_age$Census_Percent = round(age_census$Percent, 1)
tn_age$Census_Percent[10] = NA

tn_age = tn_age[,c('Age_Ranges', 'Case_Percent','Death_Percent','Census_Percent')] %>%
  tidyr::gather('Percent', 'Value', -Age_Ranges)

#Plot
fills = c('Case_Percent' = 'grey', 'Death_Percent' = '#CC0000', 'Census_Percent' = '#002D65')
ggplot(data=tn_age, aes(x=Age_Ranges, y = Value, fill=Percent))+
  geom_col(position='dodge')+
  xlab('')+
  ylab('Percent')+
  theme(panel.background = element_blank(), 
        axis.line = element_line(), 
        axis.text = element_text(face = 'bold'),
        axis.text.x = element_text(angle=30),
        legend.title = element_blank(), 
        legend.direction='horizontal',
        legend.position = 'top')+
  scale_fill_manual(values=fills, labels=c('Cases', 'Percent of Population', 'Deaths'))
```

### Confirmed Cases by Sex
```{r}
#Get Census Demographics
census_demo = 'https://raw.githubusercontent.com/mfcarrasco/COVID-TN-Counties/master/census_demographics.xlsx'

sex_census = readxl::read_excel('census_demographics.xlsx',sheet='Sex')
names(sex_census) = c('Sex', 'Census_Percent')
##stopped here####
tn_demo = 'https://myutk.maps.arcgis.com/sharing/rest/content/items/4ff6b762d64a4e0caa626df00a76c902/data'

download.file(tn_demo,'TN_Demographics.xlsx') 

tn_demo =  readxl::read_excel('TN_Demographics.xlsx',sheet=1) %>% 
  top_n(1,DATE) %>%
  select(DATE, TYPE, DETAIL, TOT_CASE_COUNT) %>%
  group_split(TYPE)

tn_sex = tn_demo[[2]] %>% 
  select(DETAIL, TOT_CASE_COUNT)

names(tn_sex) = c('Sex', 'Count')
tn_sex$Case_Percent = round((tn_sex$Count/sum(tn_sex$Count))*100,1)

tn_sex = dplyr::left_join(tn_sex[,c(1,3)], sex_census, 'Sex') %>%
  tidyr::gather('Percent', 'Value', -Sex)



fills = c('Case_Percent' = 'grey','Census_Percent' = '#002D65')
ggplot(data=tn_sex, aes(x=Sex, y = Value, fill=Percent))+
  geom_col(position='dodge')+
  xlab('')+
  ylab('Percent')+
  theme(panel.background = element_blank(), 
        axis.line = element_line(), 
        axis.text = element_text(face = 'bold'),
        legend.title = element_blank(), 
        legend.direction='horizontal',
        legend.position = 'top')+
  scale_fill_manual(values=fills, labels=c('Cases', 'Percent of Population'))
```



Column {data-width=350, data-height=450}
---------------------------

### Confirmed Cases by Race
```{r}
#Get TN Data
tn_race = tn_demo[[3]] %>% 
  select(DETAIL, TOT_CASE_COUNT)

names(tn_race) = c('Race', 'Count')
tn_race$Percent = round((tn_race$Count/sum(tn_race$Count))*100,1)

# #Plot
# ggplot(data=tn_race, aes(x=reorder(Race, Count), y=Count))+
#   geom_col(fill='#CC0000')+
#   xlab('')+
#   theme(panel.background = element_blank(), axis.line = element_line(), axis.text = element_text(face = 'bold', size = 12), axis.text.x = element_text(angle=15, hjust=1, size=12))+
#   geom_text(aes(label =paste0(Percent,'%')), vjust=-0.2)


#Donut chart
tn_race$fraction = tn_race$Count/sum(tn_race$Count) #Compute percentages
tn_race$ymax = cumsum(tn_race$fraction) #compute the cumulative percentages (top of each rectangle)
tn_race$ymin = c(0, head(tn_race$ymax, n=-1)) #Compute the bottom of each rectangle
tn_race$labelPosition = (tn_race$ymax + tn_race$ymin)/2 #Calculate label position
tn_race$label = paste0(tn_race$Race, '\n Percent: ', tn_race$Percent)


ggplot(tn_race, aes(ymax = ymax, ymin=ymin, xmax=4, xmin=3, fill=Race))+
  geom_rect()+
  coord_polar(theta = 'y')+
  xlim(c(2,4))+
  theme_void()+
  scale_fill_brewer(palette = "Spectral")
```

### Confirmed Cases by Ethnicity
```{r}
tn_eth = tn_demo[[1]] %>% 
  select(DETAIL, TOT_CASE_COUNT)

names(tn_eth) = c('Ethnicity', 'Count')
tn_eth$Percent = tn_eth$Count/sum(tn_eth$Count)*100

# #Plot
# ggplot(data=tn_eth, aes(x=reorder(Ethnicity, Count), y=Count))+
#   geom_col(fill='#CC0000')+
#   xlab('')+
#   theme(panel.background = element_blank(), axis.line = element_line(), axis.text = element_text(face = 'bold', size = 12), axis.text.x = element_text(angle=15, hjust=1, size=12))+
#   geom_text(aes(label =paste0(Percent,'%')), vjust=-0.2)

tn_eth$fraction = tn_eth$Count/sum(tn_eth$Count) #Compute percentages
tn_eth$ymax = cumsum(tn_eth$fraction) #compute the cumulative percentages (top of each rectangle)
tn_eth$ymin = c(0, head(tn_eth$ymax, n=-1)) #Compute the bottom of each rectangle
tn_eth$labelPosition = (tn_eth$ymax + tn_eth$ymin)/2 #Calculate label position
tn_eth$label = paste0(tn_eth$Ethnicity, '\n Percent: ', tn_eth$Percent)


ggplot(tn_eth, aes(ymax = ymax, ymin=ymin, xmax=4, xmin=3, fill=Ethnicity))+
  geom_rect()+
  coord_polar(theta = 'y')+
  xlim(c(2,4))+
  theme_void()+
  scale_fill_brewer(palette = "Set1")

```




About 
================================

**The Tennessee Coronavirus Dashboard**    
  
The sole intention of this Coronavirus dashboard is to provide a visual overview of the 2019 Novel COVID-19 as it relates to counties in Tennessee. The data is acquired from two different sources, and there are no guarantees on the accuracy of the data becaues of differences in numbers reported and reporting time.   

Note: This dashboard has different graphs for small screens. For more interactive graphs, please view this website on a large screen (computer/large table).   


**Data**

Data for "Cases across time in most populous counties" is a concatenation of the [New York Times Coronavirus Data](https://github.com/nytimes/covid-19-data) and the [Tennessee State Data Center](https://myutk.maps.arcgis.com/home/group.html?id=c98fc99308dd43fb98146d3cf21fc31c&q=tags%3A%22COVID-19%22&view=list&focus=files#content), which acquires its data from the [TN Department of Health](https://www.tn.gov/health/cedep/ncov.html)

Latest data from `r max(tn$date) %>% format('%m-%d')`.

Population data acquired from the [US Census](https://data.census.gov/cedsci/table?q=Tennessee%20race%20demographics&g=0400000US47&tid=ACSDP1Y2018.DP05&hidePreview=true).

Created by [Malle Carrasco-Harris](https://www.linkedin.com/in/malle-carrasco-harris).